1. Field of the Invention
The present invention relates generally to the field of human health monitoring, and more particularly to the use of multivariate models for analysis of measurements of biological parameters to provide residual-based assessment of human health indicators.
2. Brief Description of the Related Art
Medicine has for centuries been practiced as a reactive, crisis-driven process. Unfortunately, it remains largely so to this day. Chronic diseases represent a disproportionate share of the crushing economic cost of healthcare, much of which could be avoided by early warning of deterioration. Current healthcare practices are episodic and reactionary, with little visibility into patient health outside the controlled setting of the clinic or hospital. However the medical arts are only now beginning to explore out-patient telemetry from wearable devices, and there is virtually no answer to who is going to watch all this data, or how it will be analyzed to provide early warning with a low false alert rate. Moreover, out-patient telemetry poses considerable challenges due to ambulatory motion artifact and normal physiology variation in the course of daily activities not usually dealt with when a patient is sedated and supine in a hospital bed.
Other industries (nuclear, aviation, refining, computer systems) have in recent years adopted advanced intelligent algorithms for condition monitoring, that accommodate normal variation and dynamics exhibited in the sensor data collected from a target system, and differentiate it from subtle early warning signs of deterioration. One kind of machine learning technique, Similarity-Based Modeling (“SBM”) technology, has proven successful in many applications including those mentioned above. SBM is a nonparametric data driven modeling technique which learns normal behavior from multivariate data from a complex system, and distinguishes it from the onset of adverse behavior in a monitored system.
Visibility into health issues with SBM is contingent on the availability of multivariate data. Continuous telemetry from a wearable sensing device with multiple sensors could provide such data. However, existing devices are data-poor, in most instances univariate, and are primarily aimed at very narrow health related issue, e.g. glucose monitoring for diabetics, or blood pressure for hypertension. The devices are usually not meant for continuous monitoring, and any analysis performed is done using gross population statistics, i.e. not personalized to the individual. Further, current commercial telehealth devices are not easily wearable, and do not take advantage of the latest mobile technologies.
There is a need to make multivariate continuous data available for analysis, whether from a wearable device on an out-patient basis or from bedside equipment in a hospital, so that machine learning technology like the aforementioned SBM can be applied to automate early detection of incipient changes indicating the health of the patient is potentially subject to deterioration. Because medical staff is commonly overworked and short on time to spend deeply studying analytical results for each patient, especially where large populations of at-home patients may be involved, an important issue is how to summarize the results of such machine learning techniques in a simple metric for actionability.